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Solutions
Description
Compatibility Level
Clients
Product Capabilities
Use cases
EHR integrations
Client types
Differentiators
Health equity
Keywords
Media
Company details
Jump to:
Categories
Solutions
Description
Compatibility Level
Clients
Product Capabilities
Use cases
EHR integrations
Client types
Differentiators
Health equity
Keywords
Media
Company details
Sickbay
Sickbay

Categories

Solutions

Description

Product Description:

Our advanced artificial intelligence models have the highest performance in the market, building on our patented machine learning system for predicting risk.

About PulseDataAI:

PulseData is advancing healthcare with data-driven actionable clinical insights giving payers, providers and other value-based care organizations the power to improve clinical and financial outcomes, close care gaps, and improve HEDIS and CAHPS scores. PulseData leverages large, diverse datasets and deep clinical expertise to effectively identify patients with chronic conditions. Using cutting-edge machine learning and AI models, PulseData enables early intervention, equipping healthcare teams with informed, data-driven insights precisely when they matter most – at the point of care.

PulseData’s thoughtful model development avoids machine learning biases, prioritizes model explainability and ensures optimal model performance. Our tailored collaborative approach with our valued clients creates meaningful, impactful, and relevant workflows. The resulting actionable insights can be viewed on our platform and seamlessly integrated into clinical workflows to enhance and support care management workflows.

At the nexus of technology and healthcare, PulseData empowers providers to make well-informed decisions, which improve patient outcomes, reduce costs, and improve program performance. Learn More at pulsedata.ai.

Product Description:
Sickbay is an FDA-cleared clinical platform providing hospitals with the only vendor-neutral, integrated patient monitoring solution in healthcare. Sickbay consolidates disparate sourced, time-sequenced patient monitoring data with an average of 25 milliseconds per patient. Sickbay drives cost-effective patient monitoring and powers analytics by providing instantaneous and persisted physiological data to clinicians, researchers, and algorithm developers. Viewing data consolidated in Sickbay on any laptop or mobile device, on wallboards, and in on-prem or remote command centers enables clinicians to deliver the best possible care to patients throughout the hospital.
About Medical Informatics Corp:

Medical Informatics Corp.’s (MIC) mission is to deliver next-generation technologies to unify patient monitoring workflows for healthcare systems. MIC’s decade of innovation with its clinical platform, Sickbay, integrates over 30,000 vendor-neutral signals on two percent of hospital beds with over 10,000 clinical users in the United States. Most hospital systems lack native resolution data aggregated through a single source, instead deploying a multi-device strategy that silos data and data access time frames. The Sickbay Clinical Platform stands alone as the only FDA-cleared platform to aggregate time-series waveform data across disparate devices at native resolution. Current bedside devices delete data on average after 72 hours, while Sickbay saves the data indefinitely. Sickbay provides a portal through which care providers can review aggregated, time-synchronized data, annotate it, and collaborate to create algorithms (including ML & AI) and evaluate post-event care. Those algorithms and care models can then be reintroduced into the platform to advance care and innovation. Simply put, Sickbay provides the hospital teams their data from anywhere, anytime, to optimize workflows, analysis, and risk stratification across the patient care spectrum. The diverse team at MIC is fundamentally unlocking a blue ocean of new patient monitoring capabilities for ourselves, our partners, and our clients. MIC’s Sickbay saves lives, improves outcomes, streamlines strategy and technologies, and reduces the overall caring cost for hospital patients nationwide.

Compatibility level

Select which hospital or health system you work at and see a personalized compatibility level.

Clients

Select which hospital or health system you work at and see the client list

Product Capabilities

Sickbay collects, integrates, & displays time-aligned medical device data for over 30k signals. Information is displayed to end users in near real-time (~25ms) on local and mobile devices.

Sickbay's Patient Monitor (PatMon) displays per patient waveform and EHR data alerting clinical end users based on parameters defined by the hospital. Sickbay's Multiple Patient Monitor (MultiMon) displays that same information for up to 100 patients on a single screen to support virtual monitoring at scale. Sickbay's Risk Stratification module displays risk score data customized per hospital to improve awareness of patient risk in higher acuity beds. Sickbay's Alert Queue module displays alerts for telemetry patients that have been newly admitted, shift changes, dietary requirements, and rhythm validation.

Sickbay integrates EHRs using interfaces for ADT information, staffing assignments, meds, & labs. Sickbay integrates directly into the EHR to automate the capture and storage of ECG strips on the patient's record without additional, time-consuming, error-prone manual steps.

Sickbay simplifies the clinical theater by centralizing patient monitoring data from devices, EHRs, camera-enabled technology, and 3rd party analytics into a single screen. This enhances the readability and interpretation required to reduce the time-to-treat a deteriorating patient. Sickbay's interface is shared between bedside and remote care teams to drive scale and efficiency. This approach reduces the risk associated with new nursing team members on staff and improves the training / onboarding by using a single platform for patient monitoring. Sickbay's analytics workbench directly connects clinical researchers with bedside teams to develop and implement custom algorithms for specific patient cohorts in near real-time.

Sickbay integrates camera-enabled technology into a single interface alongside near real-time physiological data to provide remote care teams with video support to reduce patient risk and improve the time-to-treat for patients that are deteriorating.

Use Cases

Description:

Care Management Workflow Efficiency

Pediatric use cases:

None provided

Users:

Identifying the next best care management action and automate delivery of specific care actions directly to the point of care. 

Description:
  • Patient Monitoring
  • Remote Patient Monitoring
  • Virtual Care Models: Virtual Nursing, Central Telemetry Monitoring
  • Rounding
  • Vent Management
  • Patient Risk Stratification
  • Interdepartmental Transfers
  • Automation: ECG Strip Automation, Data Management
  • Revenue: CDI, Denials Management
  • Analytics: Compliance, Billing, Cohort Management
  • Research: Algorithm Development
  • Legal: Litigation Support
Pediatric use cases:

Same as use case descriptions above.

Users:
  • Nurses
  • Respiratory Therapists
  • Doctors
  • Anesthesioligists
  • Surgeons
  • CDI
  • Revenue Cycle
  • Research
  • Analytics
  • Quality
  • Compliance
  • Legal

EHR Integrations

Integrations:

None provided

EMR Integration & Relevant Hardware:

Recommended, but not required

EMRs Supported:

Epic, Cerner, Meditech, Allscripts, NextGen, athena, GE, eClinicalWorks, McKesson, Other, Allscripts/Eclipsys, Athenahealth, Azalea Health/Prognosis, CPSI, MEDHOST, MedWorx, QuadraMed, Self-developed, Point Click Care, Evident, Healthland

Hardware Compatibility:

Desktop, Mobile / Tablet (web optimized), Mobile / Tablet (native app), Other

Integrations:

Acute care EMR, ADT, Access +/or revenue cycle, Other

EMR Integration & Relevant Hardware:

Required

EMRs Supported:

Epic, Cerner, Allscripts

Hardware Compatibility:

Desktop, Mobile / Tablet (web optimized), Other

Client Types

Differentiators

Differentiators vs EHR Functionality:

Actionable insights integrated into clinical workflow

Differentiators vs Competitors:

PulseData’s thoughtful model development avoids machine learning biases, prioritizes model explainability and ensures optimal model performance. Our tailored collaborative approach with our valued clients creates meaningful, impactful, and relevant workflows. The resulting actionable insights can be viewed on our platform and seamlessly integrated into clinical workflows to enhance and support care management workflows.

Differentiators vs EHR Functionality:

The EHR is intended to be a point of care reference to support billing. Traditionally, only documenting discreet vitals from patient monitoring devices at single points.

Sickbay Clinical Platform:

  • captures consolidated, persistent, time-series waveform data from networked and non-networked bedside devices at its native resolution
  • re-displays near real-time, time aligned signals at an average latency of <25 mSec
  • integration of risk scores and analytics
Differentiators vs Competitors:

Sickbay is a single, interconnected platform that is designed to support dozens of use cases, therefore competition is dependent upon the use case. In addition to the data agggregation, consolidation, and visualilzation differences referenced under EHR functionality, key differentiators in patient monitoring & virtual care include: 

  • vendor neutral device integration
  • web-based to support monitoring on any PC, tablet, phone or embedded EHR workflow
  • flexible, modular design to enable ability to monitor 50+ patients across units, facilities, and vendors
  • unlimited retrospective data, from one second to one year+, to support building and sharing of trends, CDI documentation support, quality reporting
  • integration, development and deployment of near real-time risk scores and analytics into established workflows
  • automated documentation of vitals, ECG strips, and event trends into the EHR

Health Equity

Keywords

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Downloads

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PulseData Overview 2023.pdf
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Use Case: Comprehensive Case Review

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Company Details

Founded in 2016

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